Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
By Nicolae-Cătălin Ristea, Neelu Madan, Radu Tudor Ionescu, Kamal Nasrollahi, Fahad Shahbaz Khan, Thomas Baltzer Moeslund, Mubarak Shah. Computer Vision and Pattern Recognition, 2021
296 citations recorded by the source corpus.
Anomaly detection is commonly pursued as a one-class classification problem, where models can only learn from normal training samples, while being evaluated on both normal and abnormal test samples. Among the successful approaches for anomaly detection, a distinguished category of methods relies on predicting masked information (e.g. patches, future frames, etc.) and leveraging the reconstruction error with respect to the masked information as an abnormality score. Different from related methods, we propose to integrate the reconstruction-based functionality into a novel self-supervised predictive architectural building block. The proposed self-supervised block is generic and can easily be incorporated into various state-of-the-art anomaly detection methods. Our block starts with a convolutional layer with dilated filters, where the center area of the receptive field is masked. The resulting activation maps are passed through a channel attention module. Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field. We demonstrate the generality of our block by integrating it into several state-of-the-art frameworks for anomaly detection on image and video, providing empirical evidence that shows considerable performance improvements on MVTec AD, Avenue, and ShanghaiTech. We release our code as open source at: https://github.com/ristea/sspcab.
Subfields: Artificial Intelligence, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/1a7a28740c6eec40f9d8a8c3d987a8fa1b1437af
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In Artificial Intelligence
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- Dongqing Zhang
- Yang Zou
- Taewan Kim
- Yiming Zhou
- Avinash Ravichandran
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In Computer Networks and Communications
- Onkar Dabeer
- Zhikang Liu
- Dongqing Zhang
- Yang Zou
- Yiming Zhou
- Avinash Ravichandran
- Zilei Wang
- Latha Pemula
Citations in the corpus
- Deep Industrial Image Anomaly Detection: A Survey (2023, unknown): “This paper replaces the SE-layer in SSPCAB with transformer architecture.”
- SimpleNet: A Simple Network for Image Anomaly Detection and Localization (2023, methodology): “The reconstruction-based methods such as [21,31] assume that a deep network trained with only normal data cannot accurately reconstruct anomalous regions.”
- SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation (2022, methodology): “Many recent methods are proposed to detect low-level texture anomalies [35], such as scratches and cracks, which are common cases in industrial visual inspection [15,31,34,44].”
- WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation (2023, background): “Consequently, existing works have mainly focused on one-class or unsupervised anomaly detection [2,7,8,20,29,31,53,59], which only requires normal images.”